RAG & Knowledge
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Local AI-driven document search and editing with first-in-class hybrid retrieval
★ 115 starsMIT
View guide →VideoRAG — chat with your videos, even hundreds of hours of them, with graph-driven retrieval.
VideoRAG is an open-source rag & knowledge project by HKUDS (HKUDS/VideoRAG on GitHub). It has 3,389 GitHub stars and 476 forks, is released under the NOASSERTION license, and was last updated on GitHub on 18 March 2026.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
Multi-modal knowledge graphs structure video understanding instead of flat frame sampling.
Process videos up to hundreds of hours with hierarchical context encoding.
Compare and analyse multiple videos simultaneously with cross-video semantic modelling.
Text queries are aligned with visual and audio content to find specific moments precisely.
A bundled benchmark of 160+ videos / 134+ hours across lectures, documentaries and entertainment.
Designed to handle very long content on a single RTX 3090 (24GB).
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/HKUDS/VideoRAG.git
cd VideoRAGClone the repo and follow the VideoRAG-algorithm setup: conda environment, checkpoints download and dependency installation.
git clone https://github.com/HKUDS/VideoRAG.git
cd VideoRAG # see VideoRAG-algorithm for conda setup and checkpoint downloadsFollow the Vimo-desktop instructions in the repo to run the desktop experience from source.
# see the Vimo-desktop folder in the repo for its install stepsIt is maintained by HKUDS on GitHub (HKUDS/VideoRAG), where the source, issues and releases live.
No clear licence was identified from the repo metadata or README, so check the repository's licence file before relying on it commercially.
The framework is open research code; running it uses vision/LLM models as configured in its setup (local checkpoints or provider APIs), so costs depend on that choice.
Compared with frame-sampling video chat demos or generic multimodal RAG, VideoRAG's distinguishing focus is: videoRAG — chat with your videos, even hundreds of hours of them, with graph-driven retrieval. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/HKUDS/VideoRAG (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
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